As well-known in the art, media for identification, such as a resident registration card, a driver's license, a student ID card, etc., is currently being used as representative means for individual identification, user authentication, and personal information protection. However, it may be difficult to identify someone unless he or she carries these identification medium. Thus, it is not easy to confirm the identity of a person, and a person can be identified in some cases even if the person who is carrying an identification media is not the actual ID holder.
To overcome this problem, the development of biometrics technique, such as fingerprint recognition, iris recognition, face recognition, etc., is underway. Among all of the biometrics techniques, the face recognition technique is becoming popular in various applications because it is relatively less mandatory for users compared to other biometrics information and is less repulsive due to its non-contact method.
Here, human detection technology including face recognition, which is one of the biometrics techniques and is the core technology of biometrics, has been studied since many years ago, has been applied as a detection technique for biometrics, and has been recently developed in more various ways together with the expansion of the market related to digital equipment, mobile devices, etc.
In such an environment, if a camera is mounted on digital equipment, a mobile device, or the like, and the human recognition technology including face recognition is applied, the enhancement of the added value of the products and sales growth may be expected. For instance, a mobile device, such as a mobile phone, can provide a function of detecting the position of a person, recognizing the face of the person through image processing, and then changing the expression on the face; and digital equipment, such as a digital camera, can provide a function of detecting the position of a person and focusing on the position of the person. Thus, the human recognition technology can be applied in combination with various techniques.
Meanwhile, human detection algorithms including face recognition have been so far developed in the form of an algorithm which operates mainly in a PC-based environment. When this technique was applied directly to an embedded system, the detection of a human in real time was impossible or the detection rate was low due to relatively poor resource and performance, thus making it difficult to effectively detect a human.
However, as the market for home robots, such as cleaning robots, toy robots for entertainment, etc., in daily life is growing owing to the expansion of the service robots area, and the application areas capable of using human biological information in portable equipment, such as a mobile phone, a digital camera, etc., are increasing, the necessity for a high-performance real-time human detection technique in an embedded system is increasing more and more.
The conventional human detection algorithms including face recognition, however, have the problem that the human detection performance is abruptly lowered due to changes in lighting, and an additional processing procedure is required in order to solve this problem. This results in an increase in the amount of calculation and an increase in mechanical complexity, thus making it difficult to mount the algorithms on any other hardware than PC-based hardware.
Moreover, these algorithms are hard to be commercialized because the implementation of hardware consumes a lot of resources, and, even if these algorithms are commercialized, an increase in processing time caused by the increase in the amount of calculation acts as a difficult problem in real-time processing. Therefore, there is a demand for the development of a real-time processing engine which is not a simple human detection algorithm but shows high detection performance because of its robustness against changes in lighting, and which can be implemented directly on hardware at a high processing speed.